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In this episode, we hear from Bruno Aziza, Group Vice President at AI and Data Analytics at Oracle. Aziza encourages his customers to think of the AI acronym not simply as artificial intelligence, but rather as an “applied and invisible” form of augmentation that allows organizations and individuals to get to their end goals faster.

Data analysts spend about 80% of their time just preparing data before it can even be analyzed. By applying AI, that time can be cut to nearly nothing, errors can be reduced, and business analysts can reappropriate their time. This allows companies to analyze more efficiently and ask better questions to create a more productive organization.

Listen in to hear Aziza share more, including specific examples of company successes. Visit Oracle.com for more information. Aziza also encourages you to learn from customer stories and interviews in his web series, Destination:Insight, on The Oracle Analytics YouTube page.

Richard Jacobs: Hello, this is Richard Jacobs with the future tech podcast. I have Bruno Aziza, group vice president of AI data & analytics at Oracle and the website is oracle.com. Large corporations have been around a very long time. So Bruno, thanks for coming. How are you doing today?

Bruno Aziza: I’m good. Thanks for having me.

Richard Jacobs: So what do you do as a new position that looks at AI at Oracle? What kind of projects are you working on?

Bruno Aziza: Well, thank you and thanks for having me. Thanks for your listeners to take time for us. I’m a brand new to the company. I joined probably seven, eight months ago and I have worked in the data and analytics space for the last 25 years or so. And I worked at very small companies that help launch mid-sized companies, like business objects that some of your listeners might know that went public and had a great run and worked at Microsoft as well. And so I joined here to help the company essentially on a few fronts. The first one is customer-centricity. My team spends a lot of time with customers to understand how we can help them not just simplify their relationship with oracle, but also innovate at scale with data analytics in the eye. And so we spent a lot of time with that. We spend a lot of time with partners as well because like you, as you know, partners are an essential component for our success. They help customers deploy and they help customers be successful and train their people. And then finally analysts like Gartner and Forrester and many others who we work with in order to understand the customers that we don’t talk to. Analysts are very helpful in giving us a broader perspective than the perspective we might have into maybe just a customer for Oracle ecosystem. So that’s what we focus on. Customers, partners, and analysts.

Richard Jacobs: We were talking about briefly and you said you don’t call AI artificial intelligence, you call it something else. Can you go into that again? Really interesting.

Bruno Aziza: So the acronym of AI stands for artificial intelligence. As many of your listeners most know, but what we’ve noticed talking to customers is the best way to get value from AI is to think about the acronym as applied and invisible. And here’s what I mean by that. Even though there’s a lot of hype around AI and high capabilities and so forth, it really needs to connect to the value that it provides to your business people and their business process that they’re involved in, in order to impact your bottom line. And so applied is a way to think about that as a way to think about what augmented capabilities can I provide my end users. So it applies to their daily life, their daily workflow and etc. And then invisible refers to this idea that, if I told you what index in the database you’re using, how would that help you? Innovate? It wouldn’t. Right? And so I think we also have to realize that maybe sometimes we’re over-explaining AI. And the reality is that you get the most value from AI when it’s actually invisible when it’s doing or executing tasks for you, that’s, you don’t have to execute anymore. And so this concept of invisible resonates well with our customers.

Richard Jacob: Since you work with customers a lot, I think that’s great. And you’re not just in your ivory tower, you’re innovating. How did customers proceed? I don’t know. Hey, I did analytics, etc. It was me sold to them as, you know, gain powerful insights from your AI and from your database instead of. But I would think you would take a lot of handholding and instruction with customers. What do you see is the important things that need to go on so that somebody can use AI? Do they even know how to use it?

Bruno Aziza: Well, the customers that we work with, we have a unique I guess access or look into the market because we have essentially half a billion customers. So we have the largest, we have the smallest, we have a wide spectrum of customers we deal with and said there ought there a different level of maturity when it comes to the cloud when it comes to data analytics and AI for sure. But in general, there are a few themes that are coming together. I think the first one is this idea that there’s no question that data analytics and AI are the number one priority for any business. And they realize that even though they might ship products, the data they have and the way they use the data is their competitive advantage. So I think everybody’s on the same page with that. I think the issue of beyond that is how do you get to value and innovation faster than the next company? Sometimes it’s a company that’s not even in your industry that’s going to come and disrupt you. And they are, it’s what does the orchestration look like? You know, I think about a company like NHS for instance, that has saved a billion and a half using data more appropriately using analytics and predictive analytics in order to detect fraud. And so you might assume that a company like that has thousands of developers and thousands of database administrators. In fact, Nina, who is the chief data officer there started this initiative with two DVA’s and one data scientist. And so, it’s this concept of how can I have a big impact with a small team that they’re interested in looking at. They also are interested in how do I avoid the failures of the past. You know, over the last 30 years, the data and analytics world has gone through at least two transformations. The first one was highly focused on a few sets of primarily IT-centric resources and it created a bottleneck for access to value to the business. And so then we went off and bought tools that were primarily desktop-centric and that would equip business users and business analysts to build dashboards and use data and try and predict the future if you will. This third area that we’re in is a cloud-first, AI-first, mobile-first era, which really disrupts the previous two phases of this industry. So you really have to reinvent the way you think about how you’re deploying capabilities out to users. And one in particular that I think is particularly disruptive is the answer is not going to be hiring more data scientists. The answer is going to be buying software that might be more integrated, that might be more augmented, and that might be more collaborative. So the people you have today at your company can do their job faster and better. And that’s hard because the last two phases of our industry were about hiring more technical people, hiring more data scientists or training people that might be the line of business folks to become data scientists, which as you, you might know, didn’t really work out so much.

Richard Jacobs: Yeah. But who’s going to make the tools that will help people do their job more efficiently? Who’s going to create all these AI tools? I mean, I know now there are libraries starting to come about and standard algorithms and things like that. But it was like there’s still a ton of people who need to know that.

Bruno Aziza: Well, I won’t talk for the other vendors in their approach, but I think you’re right by pointing out to the fact that if the approach is a set of tools in the kitchen sink and good luck assembling them, it’s going to be fairly challenging to get to value quickly. The way we think about the market is it’s a value of the integrated solution to business users that will generate value faster. I’ll just give you a simple use case or a simple example. Did analyst spend about 80% of their time preparing data for analysis, just preparing data for analysis? That means gathering the data, cleaning the data, protecting the data. And that’s a big part of the data pipeline and that’s before any of that data can actually be put in front of the business user. Our approach has been that we can automatically take your data, clean it, prepare it, and secure it. If we identify that this is data that needs to be secured, for instance, credit card numbers, social security information, et Cetera, we don’t really need to involve humans in that task. One, It’s taking a very long time to do that. Two, it’s error-prone. And then three, frankly, those business analysts are not here. That’s not really tapping into their passion. So this entire process of how do I make your data secure and ready for analysis, we want to cut that down through augmented capabilities to no time at all and by the way, that’s the result of AI being applied. Second, once I have this information, how do I put it in from business users? Well, in the past you have to have an army of business analysts that understand that traditional desktop and download tool where you downloaded the data up to your desktop and do some great dashboards. Our approach is, well, now that I understand your data, what if I created those dashboards automatically for you? And again, that’s the result of artificial intelligence. Basically understanding the data, understanding the lineage of this data, understanding the business requirements and creating the best. So then when you can use your people to ask more questions, question the data a little bit more and maybe ask bigger questions as well.

Richard Jacobs: Well, quick question, are you saying like, I guess in the low level you would have canned reports, you know, sales by month, that kind of thing. But are you saying that you’d want to actually suggest maybe more sophisticated analysis and have them at the ready?

Bruno Aziza: Absolutely. We’re ready to do that. So let’s take an example, the number one cost in any organization is probably their people and losing the best people is often the problem. So that’s attrition. And so what if I could tell you if that if you gave me your HR data, I could tell you the cause of that attrition and I could present you with a dashboard that only would analyze, that would recommend the questions you need to ask. And probably would translate these dashboards into reports that is text that you can share across your company. We do that today and attrition is one problem, but you can think about the marketing pipeline and conversions. Why are certain things converting better than others? You know, questions that we know businesses are asking already. But the way to get there has been so inefficient that they can’t get to the answer. And so if we can eliminate the, what you might call the oxygen need, which is we all need to breathe and there are some questions your people absolutely need to have answers to, then we’re creating this space where they can now use the information that they, by the way, they should have already, but they don’t have it to ask more innovative questions, bigger questions. So ultimately you can innovate and then you can improve your bottom line. The biggest challenge our industry has, is we have a lot of data, but very little of the insights actually make it to the people that can make the decision. And if we can accelerate that, we’ll all become better companies and we’ll get better results.

Richard Jacobs: So why is that? What creates that dynamic is that people just are overwhelmed with their data and they don’t know what to do with it? Or is it that just the nature of it needs to be pushed to them, hey, look at this, hey, look at that, you know, this needs to be done for them so they don’t have to think about it.

Bruno Aziza: I don’t mean to be exhaustive in my analysis. I’m sure there’s a lot of errors I haven’t encountered yet, but I think the themes that I’ve seen are related to a few factors. The first one is that in order to have a complete analysis, your data has to come from many different sources. And today, getting your hands on a larger area of information and standardizing it and making it ready for analysis is very complicated. And so if you think about the pipeline of data and getting to artificial intelligence, very broken today, it’s not integrated at all. The second aspect of the issues is around how do you make this secure and cloud-ready so everyone can take advantage of the capabilities provided by the cloud to provide more analysis. A simple example is our models in the cloud can run with more compute. We can put in more information and compete it faster so you can generate better results. So, historically that has been challenging because people have thought about the cloud as just a place where I could just store data and maybe process it for temporal jobs if you will. And then the third aspect of it is this idea that I think from a people management standpoint, we might have put too much pressure on regular people like you and me, to have to produce the analysis before they can use it. I’ll just give you a simple example of applied AI in our mobile experience. Today if you downloaded the day by day experience for analytics the phone would know where you are, it would know your calendar, it would know your preferences. And it would give you insights as you go about your day, depending on the meetings you have next, it would push at you insights that it knows you need and it probably finds insights that maybe you haven’t even thought about asking for. And so I think we have to shift that mentality and we now have the technical capabilities to make it happen. There’s also a timing aspect, right? Describing what I’m talking about today, 10 years ago would have been particularly hard. And a lot of it is related to the availability of the cloud and the availability of those open models that you’re referring to.

Richard Jacobs: So any particular insights or data analyses that you’ve seen in the case studies where you thought the result was super instructive or really cool? Or the customers want the same?

Bruno Aziza: Oh, there are many. In fact, as you know, we’re preparing for Oracle Open World and we have lots and lots of sessions that are extremely innovative. There is the example of GE, for instance, which is putting together a blockchain and autonomous did our warehouse and Oracle analytics in order to create a ledger. It’s really innovative people putting these technologies together. There’s the example of FedEx, the city of Las Vegas and many other organizations that are, thankfully you’re not at the stage of maturity where they can push the envelope if you will a little bit and get more information to more people faster. I mean, another example is the example of a riverbed as a company. We’ll have the stage on our solution. Keynote is a company that was trying to get insights out to their salespeople and they couldn’t get to them because it required for people to get analysis and get services off of their desktop or the web. And they’ve deployed a mobile experience that gets data to folks before they even get to the meeting. And it gets them in the format that they like and it predicts when they’re going to run out of the pipeline and then create a pipeline for them based on the attributes of customers that they have. So you can imagine, you’re on the system making sure that the data’s got your back if you will enable you to win in the marketplace. I have to say we’re also working here at Oracle. We’re in a unique situation where we’re in the middle of the database world in the middle of an application. Well, when we get to see large amounts of data that are securely stored in the cloud with a large amount of folks using applications for their daily workflow. So we’re brought into those use cases that maybe other vendors don’t get to see.

Richard Jacobs: Maybe a one more use case that you think is, was pretty cool and instructive now, whenever you could say about that.

Bruno Aziza: Well, you know, and some of them are stories that we’re keeping for the event, but I’ll give you maybe another one that I thought was interesting. This is a skin scout, a construction company and that has a very large dataset. And they have SQL server data. They’ve got oracle database data, they’ve got external data sets and they were able to identify in construction the issue is injuries does huge liability when you’re working on the project and you want to deliver an on time and on budget when you have these unknowns and they weren’t able to correlate the particular user types and behaviors and the type of injuries they would get. So they provided basically to fix their training in order to prevent the injuries before they occur. I know it’s Kind of crazy to think about that way, but we have a lot of those use cases where companies are, you know, it’s kind of like they get to a point of certainty where, I mean, I like to call it, it’s like Christmas, you know, it’s going to happen on December 25th. So why don’t you use the data to guide a path that’s going to give you a good resolution? Another example is the company Cecil in Italy, is able to build a set of algorithms that allows them to identify fraud before it occurs. Today you might have a credit card and they call you when fraud has been detected. What if it is activated the card before someone got their hands on your card and sends you a card ahead of that too. Our fraud systems today are very reactive. And so here you have a company, again, it’s not a very large company, but because they have the right technology in the right mindset, they’re able to provide analysis and algorithms that predict fraud before it occurs. It’s really powerful.

Richard Jacobs: I like your example with a construction company. People that hang drywall for some reason, they’re getting all these particular injuries. So then you find the holes in their safety training protocol and you add on a special module just for them. And that should reduce their injuries.

Bruno Aziza: That’s right.

Richard Jacobs: I think that’s a really smart tool to have something like that. That’s great.

Bruno Aziza: Absolutely. And every industry listening has an example just like this. If you think about the areas of inefficiencies that you have throughout your day and we don’t have to talk about just injuries, right? But you have to talk about, I mean, just think about meetings for instance, what is the most efficient way to have a meeting about a topic to make sure the team gels together. Wouldn’t it be beautiful if we had fewer meetings that are more efficient and we got more out of them?

And we can do that today. We can analyze that data, we can analyze emails. We can now analyze follow through, there’s a lot of data that’s available. But like I was saying earlier because the pipeline to get the analysis out is so broken. We can’t get to that data. And because we can’t get to those insights, we can’t improve our company.

Richard Jacobs: I was going to give you an example of meetings. It’ll be cool if you had a tool where you put in the meeting you want and all the different agenda items. And then the AI system suggests who should and shouldn’t be a party to those discussions because of their expertise. Are there certain projects and then it can sort the meeting in order. So certain people that don’t have a lot of time, you can tell them your schedule for these 10 minutes only instead of sitting there for an hour. I mean the meaning planning software like that would be like, so anyone let people know like relevant people based on what happened in the meeting. Maybe it could take a transcript and look for keywords and it can do all kinds of stuff. Just within meetings I’m thinking.

Bruno Aziza: Oh, absolutely. And what you’re describing, I don’t know if you read the book Principles by Ray Dalio, but he’s describing how he’s built this. This is a great book for your listeners. It’s a big book, but Ray Daleo runs the largest hedge fund in the world and he’s a very famous guy. And his approach to data is very unique. And not only does he have a way to analyze the decisions made in meetings, but he also has this concept he calls idea meritocracy where he’s able to basically change decisions based on where the idea about that decision came from. For instance, I’m a French person that grew up in the south of France. My opinions about food in the south of France might rank higher than someone that might be from different geography that’s never lived in France. I’m thinking a stupid example here just to make a point, but he’s got it down to that level where it particular decision or particular opinion about a decision would be weighted more so that you can get a better output. And ultimately, you mean the way to get there? We’re not very far from that. We’re not very far from being able to get there. Now read value is an exception and, and he has a great book and then he also even has an app I think, that everyone should take a look at. But it’s a good aspiration, I think for all of us. There’s one resource we cannot create and it’s time. And if we can save that we can become a better society and we can create better companies.

Richard Jacobs: Has anyone tried to tune an AI so it makes decisions like someone makes decisions? I mean, could you make like a Ray Dalio let’s say you kind of know like he tends to be conservative and intends to favor groups over this. Is there any central point of aligning an AI with a personality so that it kind of makes decisions in the spirit of how they would?

Bruno Aziza: I think this will be very hard in general. And I if you read some of the theory on AI there’s a lot of scary stuff out there saying, oh my gosh, the machines are going to completely replaced humans. You know, I don’t see that happening and the reason for that is if you step back from what the relationship between humans and machine has been and what is most likely, it will continue to be. It goes off three types of relationships. The first one is what you’re describing, which is automation. And there are some tasks that are highly repetitive, a binary in nature that has a large, and they’re largely not nuanced, right? So a machine can learn a lot, but from a machine to get to judgment. It’s the particularly complicated path there. But automation is clearly is it brings a lot of value. The second relationship is augmentation, which is where the majority of the work is going to happen. Think about it as intellectual augmentation. Just like we think about augmented physical capabilities like having prosthetic arm or anything like that. When something’s missing, we have now technology that can add to it and certainly did AI is from an augmentation standpoint, can allow us to get to the end goal a lot faster. It’s like some of the examples that we talked about, there is one area though that the machine cannot produce and it’s collaboration. And collaboration primarily occurs humans to humans, right? So the first one automation was machine to machine. The second one augmentation in humans plus machines. The third example here is human to human. I mean this is going to be really hard to replace because what drives effective collaboration is primarily related to intuition. Primarily related to chemistry and the ability to have judgments and ethical guides and so forth. That is really hard. One I think to hard code and second really hard for a machine to be effected and learning constantly on that third day or year I think is going to be particularly a hard one to pass. And frankly, I don’t think it’s an advisable one to try and replace.

Richard Jacobs: That makes sense. So what’s ahead for Oracle? Or for the AI assistant industry itself. What do you see in the next few years is possible?

Bruno Aziza: What we see is that AI is going to permeate through every piece of software that is used today. I start with the database and self-maintaining itself, patching, being available. So the data is not just secured but it’s also available. Can you process at scale on a very large speed that’s really important. Second is on the analytics layer on making sure that we can provide better analysis faster to business users. And then in the application world itself, how can we automate some of the prostitutes that today did not bring in a lot of value? How can we predict when the defect is going to occur and put decisions towards a path of success because we know the information and then increase the availability and the value that we get out of our applications. I mean an uneasy example that we’re going to have a transcript of this conversation I’m sure, but what if the entire transcript of this information can be available inside an application, be matched to a record and help me create the next conversation. I think that’s where we’re going. The data is there. And the applications are there. What’s missing is integration. And so I’m the CIO or chief data officer listening to this, I would look at how you can best partner with vendors that provide you with this level of integration. Because I said, ultimately you’re not buying AI. You’re buying the result of the AI and the results of AI is integration in what you’re already doing. We don’t want to turn your business users into AI specialists. If we did that, it would mean that we would have learned nothing from the prior waves of data and analytics and we would create more bottlenecks and more frustration.

Richard Jacobs: That makes sense. I mean the whole goal is to help an organization seamlessly be more efficient. Be more informed, etc.

Bruno Aziza: That’s really the irony. I’ve been in this space for a long time and I laugh when we look through product strategy and I look at the persona sometimes that we build product score and there’s always this imaginary persona of the business analysts where there are thousands of them inside the organization. I don’t think that’s true. I think there are millions of information workers that need better insights faster. And we need to build for them. We, of course, need to build for the specialist, but we can’t forget that today the data and analytics and the AI capabilities are only available to 35% of your employees. There’s a silent majority here of 65% that are still chasing the insights and they’re making decisions. I like it as a pre-history, so we’ve got to go out and fix that. A lot of it is the consequence of where this industry has been, some of it is unfair because the majority of technology wasn’t available, but we call this the Las Vegas effect. What happens in your BI tools staging your BI tool? That’s not right. That’s not how we can make better decisions. And so integration is, I think is the answer.

Richard Jacobs: I want to give you an example. My mom works for Chase Bank for a number of years and she said new people would come in and they hired the young people and they would have them in sales and marketing and all that stuff. And I said, well yeah, JP Morgan Chase has been around for a hundred plus years. You would think they would have, if they were smart, they would have saved like all their advertising, all their marketing. They did all their customer intelligence and having it at the ready for new trainees. And she was like, no, not even close. But things like that, institutional memory and learnings, they’re probably lost without someone like aggregating them, like certain companies that have maybe a museum of what they’ve done before. But all that history, all that knowledge would just the pain in the way for future work with them.

Bruno Aziza: Absolutely. No, in every industry is like this. I actually bank with the chase, so I’ll give you a call after this to see if I could get a better rank. But I think it’s true of even there’s every industry and I think some of it is a consequence of, it used to very expensive to store data. Now it’s very cheap and so the mentality has to change from, hey what are the questions we’re going to ask? And that makes sure that we’re just storing the data that’s going to enable us to ask this questions to, we don’t know what we’re going to ask, so let’s just stole all the data and see what happens. We’re now in a position to be able to do that. But 10 years ago that wasn’t the case at all. And so, it’s going to take a cycle, but the approach absolutely today can be, 90% of the questions that are going to differentiate you, you probably don’t know what they are. So just assume and store all of that. Use augmentation capabilities and AI to maybe ask those questions cause the cost of the question once you have a model like that is nothing. So why not ask it? Right. I think it’s probably the best way to get, I could summarize it. Think about your company today. If you’re listening to your show, what is the cost of one asking question? What is the cost of making the wrong decision and what is the cost of not asking the question? I had a customer do this recently and he told me the cost of not asking the question was about $50,000. So it’s a manufacturing company. And so can you imagine, what are all the questions you’re not asking today that you could be asking if you had the data and if you had the technology to enable you to answer them. That’s the innovation gap.

Richard Jacobs: Yeah. I mean, I could certainly see that people would assume, well, why asking because we can’t find out or it’s very hard to find out.

Bruno Aziza: No, it’s hard. It’s hard to get to it. So think about that, right. That’s the innovation gap inside your enterprise. You’ve got the perfect people, they have ideas and they want to ask all these questions but they can’t get the information. They can get the technology too. That’s really a shame. And so once we fixed that and it will never be fixed, right? Because the more we learn, the more we ask questions. That’s what’s interesting about this industry is every question leads to another six questions. But once we can create that process to be fluid augmented by the machine, so it’s faster we’ll get some really interesting results. I mean, we’ll solve diseases. We’ll work on really big problems rather than just trying to understand what my inventory was yesterday. Maybe we’ll be able to answer what should be the inventory in the first place.

Richard Jacobs: Well that’s very cool. Really good stuff to think about. You’re well-spoken about this and it makes total sense. So any closing remarks and I’m going to ask where people can find you, but you know, its oracle.com.

Bruno Aziza: First of all, thank you so much for the interview today and thanks for listening if you are still here after all this babbling have been doing but there are many ways you could get in touch with us. Obviously Oracle.com is the place you should go to. We also are very customer-centric and focus on the stories of customers. We want to make sure that everyone listening to your show today can also listen to our customers. So we’ve created this program called destination insight. Every week I interview a new customer that tells you about their journey. And so I would say if there’s one thing, just go on, check that out and connect with those people. You’re going to learn a lot from them. And the best way to learn is to learn from other people’s mistakes. Don’t make the same mistake twice.

Richard Jacobs: Okay, well, hey, you just slip something in at the very end. I wanted to ask you. You said you interview a customer every week and what do you ask them about their journey and how they ended up working with you guys or what are you asking?

Bruno Aziza: Yes, that’s correct.

Richard Jacobs: That must be an interesting one. What jumps out at you from doing that? I mean, you busted out all kinds of unusual insights.

Bruno Aziza: Oh yeah. Oh, I mean, Cecil, the company has talking about it is the company we featured last week, the week before we had Coca-Cola. So, here’s an easy example of where you can predict where your customers are going to be. So Coca-Cola has got a set of customers, they all share the same attributes or similar attributes. He used our technology to create clusters based on those clusters, offer them specific options and based on those options, figure out the conversion so he could direct its sales efforts towards the right types of customers, the right regents, the right products, et Cetera, et cetera. So you find out all these stories from customers, large companies that you think have it all figured out where they now are coming up with new innovative ways of driving revenue and getting innovation out to the marketplace. It’s really interesting. And it’s also worldwide customers as well. I mean, we have customers from every geography. We have Coca-Cola, this one is in Italy. I was talking to another customer in large bank in the UK that had a thousand users on-premise and moved to tens of thousands of users in our cloud and managed to save money. I mean there’s a lot of tips and tricks. There is Daimler Benz who was explaining how they’re able to challenge the AI. So, here we are talking about, oh my gosh, the machines are going to take over. Well, Daimler has actually gone the other way where they are running algorithms and they’re using humans to challenge the machine. So you’ll find interesting stories. Those videos are quick, right? So they are not meant to be exhaustive. But what I’m trying to create is, for the people who are listening here, don’t just come to me, go to the folks that I’ve succeeded, Reach out to them and then try and learn from them as well. That’s the best way to do it.

Richard Jacobs: Okay, that’s great. Well it was great talking to you and I learned a lot of cool stuff and I appreciate you being here.

Bruno Aziza: Well. Thank you so much for the interview today, and I’m sure we’ll be in touch.